Grain price support policy and the distortion of market price

Abstract

Purpose

This paper aims to examine whether China's grain price support policy has distorted the grain market price.

Design/methodology/approach

The time-varying differences-in-differences (DID) model is used to study the impact of support policies on grain prices, and it is combined with the event study method to explore the dynamic effects of price support policy. Panel data model is used to study the effect of the price support policy on price formation for national grain market prices. In addition, we apply the smooth transformation (STR) model to verify whether there is a distortion in the transmission of grain prices among different markets in China and from the international market to China’s market.

Findings

China’s grain price support policy plays a significant role in rising grain market prices, weakens the decisive role of the market mechanism in the formation of grain prices, hinders the spatial transmission of market price signals and decreases the effect of price transmission from the world market to China’s market.

Research limitations/implications

In order to ensure both the stability of grain production as well as the market stability, and also to ensure that intervention policies do not distort the food market, the minimum purchase price of grain and market regulation policies should be adjusted as follows: (1) price support policy should be shifted to an income support policy and (2) reasonably determine the scale of reserves and implement a grain minimum purchase price policy in limited areas.

Originality/value

Our findings are relevant for understanding the effect of China's grain price support policies on the implementation regions and the price transmission effect, which provide reference experience for developing countries to implement food price policies.

Citation

Tan, Y., Yue, R., Chen, L., Li, C. and Chen, K.Z. (2024), "Grain price support policy and the distortion of market price", China Agricultural Economic Review, Vol. 16 No. 3, pp. 507-526. https://doi.org/10.1108/CAER-06-2023-0159